{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2lzh as d2l\n",
    "from mxnet.gluon import data as gdata\n",
    "import sys\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train = gdata.vision.FashionMNIST(train=True)\n",
    "mnist_test = gdata.vision.FashionMNIST(train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mnist_train), len(mnist_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature, label = mnist_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 28, 1), numpy.uint8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.shape, feature.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, numpy.int32, dtype('int32'))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label, type(label), label.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'derss', 'coat', \n",
    "                  'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_fashion_mnist(images, labels):\n",
    "    d2l.use_svg_display()\n",
    "    _, figs = d2l.plt.subplots(1, len(images), figsize=(12, 12))\n",
    "    for f, img, lbl in zip(figs, images, labels):\n",
    "        f.imshow(img.reshape((28, 28)).asnumpy())\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = mnist_train[0: 9]\n",
    "show_fashion_mnist(X, get_fashion_mnist_labels(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取小批量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "transformer = gdata.vision.transforms.ToTensor()\n",
    "if sys.platform.startswith('win'):\n",
    "    num_workers = 0 #不用额外的进程来加速读取数据\n",
    "else:\n",
    "    num_workers = 4\n",
    "\n",
    "train_iter = gdata.DataLoader(mnist_train.transform_first(transformer),\n",
    "                             batch_size, shuffle=True,\n",
    "                             num_workers = num_workers)\n",
    "test_iter = gdata.DataLoader(mnist_test.transform_first(transformer),\n",
    "                            batch_size, shuffle=False,\n",
    "                            num_workers = num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.31 sec'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = time.time()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "'%.2f sec' % (time.time() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# softmax回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2lzh as d2l\n",
    "from mxnet import autograd, nd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs = 784\n",
    "num_outputs = 10 \n",
    "W = nd.random.normal(scale=0.01, shape=(num_inputs, num_outputs))\n",
    "b = nd.zeros(num_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "W.attach_grad()\n",
    "b.attach_grad()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实现softmax运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[5. 7. 9.]]\n",
       " <NDArray 1x3 @cpu(0)>, \n",
       " [[ 6.]\n",
       "  [15.]]\n",
       " <NDArray 2x1 @cpu(0)>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.array([[1, 2, 3], [4, 5, 6]])\n",
    "X.sum(axis=0, keepdims=True), X.sum(axis=1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    X_exp = X.exp()\n",
    "    partition = X_exp.sum(axis=1, keepdims=True)\n",
    "    return X_exp / partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[-1.0057124  -1.1259842  -0.32546774  0.4900462  -1.0509403 ]\n",
      " [ 0.3378331  -0.73851013 -0.4733924   0.9529923  -0.654586  ]]\n",
      "<NDArray 2x5 @cpu(0)>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[0.10776395 0.09555208 0.21276492 0.48092043 0.10299859]\n",
       "  [0.24963908 0.08508689 0.11091795 0.4618201  0.09253594]]\n",
       " <NDArray 2x5 @cpu(0)>, \n",
       " [1.         0.99999994]\n",
       " <NDArray 2 @cpu(0)>)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = nd.random.normal(shape=(2,5))\n",
    "print(X)\n",
    "X_prob = softmax(X)\n",
    "X_prob, X_prob.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def net(X):\n",
    "    return softmax(nd.dot(X.reshape((-1, num_inputs)), W) + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[0.1 0.5]\n",
       "<NDArray 2 @cpu(0)>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = nd.array([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y = nd.array([0,2], dtype='int32')\n",
    "nd.pick(y_hat, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy(y_hat, y):\n",
    "    return -nd.pick(y_hat, y).log()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "计算分类准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_hat, y):\n",
    "    return (y_hat.argmax(axis=1) == y.astype('float32')).mean().asscalar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(y_hat, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(data_iter, net):\n",
    "    acc_sum, n = 0.0, 0\n",
    "    for X, y in data_iter:\n",
    "        y = y.astype('float32')\n",
    "        acc_sum += (net(X).argmax(axis=1) == y).sum().asscalar()\n",
    "        n += y.size\n",
    "    return acc_sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1141"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_accuracy(test_iter, net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs, lr = 10, 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 1.3643, train acc 0.646, test acc 0.689\n",
      "epoch 2, loss 0.9173, train acc 0.717, test acc 0.736\n",
      "epoch 3, loss 0.8045, train acc 0.748, test acc 0.760\n",
      "epoch 4, loss 0.7446, train acc 0.766, test acc 0.773\n",
      "epoch 5, loss 0.7052, train acc 0.777, test acc 0.785\n",
      "epoch 6, loss 0.6766, train acc 0.786, test acc 0.791\n",
      "epoch 7, loss 0.6543, train acc 0.792, test acc 0.798\n",
      "epoch 8, loss 0.6365, train acc 0.797, test acc 0.801\n",
      "epoch 9, loss 0.6217, train acc 0.801, test acc 0.805\n",
      "epoch 10, loss 0.6092, train acc 0.805, test acc 0.808\n"
     ]
    }
   ],
   "source": [
    "def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,\n",
    "             params=None, lr=None, trainer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0\n",
    "        for X, y in train_iter:\n",
    "            with autograd.record():\n",
    "                y_hat = net(X)\n",
    "                l = loss(y_hat, y).sum()\n",
    "            l.backward()\n",
    "            if trainer is None:\n",
    "                d2l.sgd(params, lr, batch_size)\n",
    "            else:\n",
    "                trainer.step(batch_size)\n",
    "            y = y.astype('float32')\n",
    "            train_l_sum += l.asscalar()\n",
    "            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()\n",
    "            n += y.size\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' \n",
    "             % (epoch + 1, train_l_sum / n, train_acc_sum /n, test_acc))\n",
    "        \n",
    "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size,\n",
    "         [W, b], lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X, y in test_iter:\n",
    "    break\n",
    "\n",
    "true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())\n",
    "pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())\n",
    "titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",
    "\n",
    "d2l.show_fashion_mnist(X[0:9], titles[0:9])"
   ]
  }
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